Our work builds toward recent works for learning a generalizable continuous control policy that is morphology-agnostic [1]}, [2]} or object-agnostic [3]}, [4]}. Contrary to these works, our method enables diverse morphologies or embodiments through tool use, arguably a more natural way to build up rich manipulation capabilities in the real world. We hope that this can help lead to a single policy that masters all hierarchies and embodiments of manipulation.
